Sustainable ras balancing based on predictive models

Abstract

Methods, systems, and products for sustainable Reliability, Availability, and Serviceability (RAS) balancing based on predictive models includes predicting, based on one or more predictive models, a potential failure within a computing system, and provisioning, based on the potential failure, one or more redundant resources for failover.

What is claimed is: 
     
         1 . A method of sustainable reliability, availability, and serviceability (RAS) balancing based on predictive models, the method comprising:
 predicting, based on one or more predictive models, a potential failure within a computing system; and   provisioning, based on the potential failure, one or more redundant resources for failover.   
     
     
         2 . The method of  claim 1 , wherein the one or more redundant resources provisioned are selected based on a type of the potential failure. 
     
     
         3 . The method of  claim 1 , wherein the one or more redundant resources provisioned are selected based on a predicted time of the potential failure and on a predicted time for provisioning the one or more redundant resources. 
     
     
         4 . The method of  claim 3 , wherein the predicted time of the potential failure is determined based on the one or more predictive models, including monitoring one or more of: one or more system variables and one or more system metrics. 
     
     
         5 . The method of  claim 3 , wherein the predicted time for provisioning the one or more redundant resources is determined based on the one or more predictive models, including monitoring one or more of: one or more system variables and one or more system metrics. 
     
     
         6 . The method of  claim 1 , wherein the potential failure comprises a component failure of a component included within the computing system. 
     
     
         7 . The method of  claim 1 , wherein the potential failure comprises a system-wide failure of the computing system as a whole. 
     
     
         8 . The method of  claim 1 , further comprising, after a period of time has passed after provisioning the one or more redundant resources, deprovisioning the one or more redundant resources. 
     
     
         9 . The method of  claim 8 , wherein deprovisioning the one or more redundant resources includes, if a failure occurs during the period of time, deprovisioning only the one or more redundant resources that are not required responsive to the failure. 
     
     
         10 . The method of  claim 1 , wherein predicting the potential failure includes performing machine learning. 
     
     
         11 . The method of  claim 1 , wherein predicting the potential failure is based on one or more artificial-intelligence models. 
     
     
         12 . The method of  claim 1 , further comprising:
 if a failure is detected, predicting whether a system-wide failure affecting the computing system will occur; and   provisioning a complete backup system for failover of the computing system.   
     
     
         13 . A computer program product comprising a computer readable storage medium and computer program instructions stored therein that, when executed, are configured to:
 predict, based on one or more predictive models, a potential failure within a computing system; and   provision, based on the potential failure, one or more redundant resources for failover.   
     
     
         14 . The computer program product of  claim 13 , wherein the one or more redundant resources provisioned are selected based on a predicted time of the potential failure and on a predicted time for provisioning the one or more redundant resources. 
     
     
         15 . The computer program product of  claim 14 , wherein the predicted time of the potential failure is determined based on the one or more predictive models, including monitoring one or more of: one or more system variables and one or more system metrics. 
     
     
         16 . The computer program product of  claim 14 , wherein the predicted time for provisioning the one or more redundant resources is determined based on the one or more predictive models, including monitoring one or more of: one or more system variables and one or more system metrics. 
     
     
         17 . The computer program product of  claim 13 , further comprising, after a period of time has passed after provisioning the one or more redundant resources, deprovisioning the one or more redundant resources. 
     
     
         18 . The computer program product of  claim 17 , wherein deprovisioning the one or more redundant resources includes, if a failure occurs during the period of time, deprovisioning only the one or more redundant resources that are not required responsive to the failure. 
     
     
         19 . A system comprising:
 a computing system;   a plurality of redundant resources; and   a processor configured to:
 predict, based on one or more predictive models, a potential failure within the computing system; and 
 provision, based on the potential failure, one or more redundant resources for failover. 
   
     
     
         20 . The system of  claim 19 , wherein the plurality of redundant resources includes one or more of: duplicate paths to a destination target, replicated storage, RAID storage, redundant componentry, and software monitors.